2014-2022 NBA Data
with a focus on the Boston Celtics
" Anjali Tanna
The stars of the NBA
The stars of the NBA
The reasoning behind my choice of this dataset has to do with my passion for basketball and the NBA. I love watching basketball games as well as reading all of the game statistics right after. Seeing how quickly an NBA player's stats can fluctuate and how the top leaders change daily is something that is super interesting to me. I would like to compare different stats amongst teams and players through the past couple of seasons to see who the top players are in the NBA. These types of visualizations and analysis would provide helpful insight for game managers to see whether players are doing the best in order to increase their contract or figuring out which players are playing the worst and deserve a trade.
This dataset was found on basketball-reference.com and it includes data from the 2014-2022 seasons of the NBA. It was interesting to see the change in trends for points per game depending on the year, leading scorers, and other basketball statistics. I also found it interesting to see the difference in stat trends as time goes on, since the game of basketball is constantly changing. The skill levels of NBA players and athleticism is constantly increasing as time goes on and by exploring this dataset, I am able to visually showcase this. It is also interesting to see how the players are getting traded, as they have statistics for multiple teams. Overall, using NBA data allows me to make complex and interesting visualizations.
Streamgraph
This streamgraph is displaying the sum of points per age group based on the season. The marks used are lines and the channels used are area and color. The color indicates the age that is being described. The x-axis indicates the season and the y-axis shows the sum of points for that age category. There is also an interactive element as there is hover data that displays the age, sum of points, and the season. The reason I chose to use color in this graph was because it is the only efficient way to show the differences in the ages.
Box and Whisker Plot
This box and whisker plot showcases the breakdown of ages across the NBA teams. The box and whisker plot shows the locality, spread, and skew of ages across NBA teams. It is interesting to see which teams have older ages compared to those with younger teams. It is also interesting to see the outliers, as they are all generally older players. The marks used in this visualization are points and lines. The channels used are area, position, and color. I chose to incorporate color into this visualization just for aesthetic purposes.
Bubble Plot
This visualization shows the sum of points per player based on the season, team, and position. This visualization utilizes two dropdown menus, one to select the team and one to select the position. The marks used in this visualization are points and the channels used are color and size. I used color to show the players field goal percentage. The darker the blue, the better the player’s field goal percentage is. As for size, it represents the amount of total points scored by that player in that season. The bigger the circle, the more points scored.
Scatter Plot and Bar Chart
This brushing and linking scatter plot and bar chart shows the minutes played per player vs. the amount of points scored. When a specific interval is selected on the scatter plot, a corresponding bar chart will show on the bottom. This bar chart displays the sum of points per team based on the minutes played interval that is selected. The marks used on the scatter plot are points and the marks on the bar chart are lines. The channels used for both are position, length, and color. I used color to make it easier to see which player belongs to which team.
The topic of my infographic is NBA player statistics from the 2014 season through the 2022 season. This is an extremely interesting topic due to the fact that NBA player statistics can change drastically within one game. For example, a player could be shooting 65% throughout the entire season but with two poorly played games, their field goal percentage could drastically decrease. This is important because it can change the directory of their career, their contract, what team they play for, and how their coach will determine their minutes in the game. Through my infographic, I want users to see the best players and teams based on their statistics throughout the years. I want viewers to also learn the statistics that are less based on the game of basketball, but the players themselves (i.e., age). Overall, I want to create a compelling infographic on NBA player statistics that will want the viewer to wonder how drastically it will change through the duration of the next season.
